Getting Started with Notebook

This article provides step-by-step guidance on getting started and offers links to various resources necessary for using the Syntasa Notebook.

• **Understanding the File Management within the Syntasa Application**The Syntasa platform offers the functionality to directly upload files to your S3 bucket from within the application. These uploaded files can be seamlessly utilized by notebooks for various read and write operations.
• Uploading files directly into the cloud storage from the Syntasa application
  This article presents a step-by-step guide on how to upload files directly into an S3 bucket through the Syntasa application.
• **Syntasa Notebook Overview**This article provides an overview of the Syntasa Notebook feature, outlining its purpose and key functionalities.
• How to create a Syntasa Notebook
  A comprehensive guide is provided for creating a Syntasa Notebook, covering all necessary fields and settings.
• How to Update a Syntasa Notebook
  This detailed article explains the process of editing notebook details once the notebook has already been created.
• How to share notebooks with other users
  Learn how to share notebooks with other users through this article's comprehensive explanation.
• How to attach a runtime to the notebook for GPU
  By default, the selected language kernel is utilized for code testing. However, for tasks requiring significant computational resources, Syntasa offers the option to attach a runtime cluster for heavy-duty jobs.

Launching the Notebook

A comprehensive guide has been included regarding the launch of the Syntasa notebook. For further information, you can consult the article "Launching the Notebook."

Once you launch the notebook, you may need help with some code to perform actions. We have created a JupyterLab sample notebook with frequent use cases.

Here is the list of JupyterLab sample notebooks:

• **Getting Started with SparkSQL**This tutorial introduces Spark SQL programming within a JupyterLab environment (assuming Python is used). This article covers how to: Query Hive Tables, Filter & Aggregate Data, Work with Spark DataFrames, Convert to Pandas DataFrames, etc
• Reading & Writing a CSV File
  This article guides a user on how to get started with reading data from a CSV.
• Importing Libraries in JupyterLab: A Beginner's Guide
  This guide will help you understand how to import libraries and get started with using them in your JupyterLab notebooks.
• Getting Started with TensorFlow
  This code provides a basic example of training a binary classification model with TensorFlow in JupyterLab.
• Credentials Store in Notebooks
  Introduced in Syntasa 6.3, the credentials store enables you to create, save, and share credentials as an object so that other users can use them without the details being revealed. This sample demonstrates how to create and utilize credentials within a notebook.

Sample Syntasa Notebooks

• Installing Libraries within Notebooks (AWS)
  Applicable to Syntasa platforms installed in an Amazon AWS environment, this sample notebook provides examples of installing libraries within your notebook.
• File Download from S3 into Notebook (AWS)
  Applicable to Syntasa platforms installed in an Amazon AWS environment, this article notebook provides examples of how to download files from S3 to your notebook. Downloading files from sources other than S3 will be done similarly.
• File Download from S3 into Syntasa Notebook With Runtime (AWS)
  Applicable to Syntasa platforms installed in an Amazon AWS environment, this sample notebook provides examples of how to download files from S3 to your runtimes attached to your notebook. Downloading files from sources other than S3 will be done similarly.